• Advanced Machine Learning Clas...

Treffer: Advanced Machine Learning Classifier 2.1.0

Title:
Advanced Machine Learning Classifier 2.1.0
Authors:
Kottilapurath Surendran, Gokul, orcid:0000-0002-1360-, Mokroš, Martin
Publisher Information:
Zenodo
Publication Year:
2025
Collection:
Zenodo
Subject Terms:
Machine learning, Supervised learning, Easy ML Tool, Convolutional Neural Network, Image Classification, Data Preprocessing, Image Augmentation, Jupyter Notebook Generator, Python Script Generator, Model Training, Train-Test Split, Computer Vision, AI Tool, Educational Tool, Automated ML Workflow, AI Research and Development, Feature Extraction, AdaBoost, Naïve Bayes, Random Forest, Support Vector Machines (SVM), Image Analysis, Confusion Matrix, Classification Report, Visualizations, Bagging Algorithms, Misclassified Images, Image Prediction, Data Science Tools, Advanced Classifier
Document Type:
E-Ressource software
Language:
unknown
Relation:
https://zenodo.org/communities/reforest/; https://zenodo.org/communities/eu/; https://zenodo.org/records/16734958; oai:zenodo.org:16734958; https://doi.org/10.5281/zenodo.16734958
DOI:
10.5281/zenodo.16734958
Availability:
https://doi.org/10.5281/zenodo.16734958
https://zenodo.org/records/16734958
Rights:
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number:
edsbas.9FB620A9
Database:
BASE

Weitere Informationen

The Advanced Machine Learning Classifier is an intuitive software designed to simplify complex image classification processes. Combining state-of-the-art algorithms with an easy-to-use interface, it empowers users to analyze, classify, and visualize image data seamlessly. Key Features Image Analysis: Load and process JPG, JPEG, and PNG images effortlessly. Customizable Image Dimensions: Resize images to preferred dimensions, such as 150x150 or 250x250 pixels, to suit specific requirements. Comprehensive Preprocessing Options: Choose from various preprocessing techniques, including: Normalization, Standardization, and Augmentations. Advanced Feature Extraction: Utilize powerful Convolutional Neural Networks (CNNs) for feature extraction. Users can build and customize their own CNN models, with the flexibility to add or remove convolutional layers based on their specific needs. Diverse Algorithms: Choose from various machine learning algorithms, including AdaBoost, Decision Trees, Gradient Boosting, Naïve Bayes, Random Forest, and Support Vector Machines (SVM). Custom SVM Kernels: Customize SVM kernel functions for tailored results. Boosted Algorithms: Leverage bagging to enhance algorithm performance. Flexible Train-Test-Validation Splits: Adjust dataset splits for training, testing and validation. Error Analysis: Identify misclassified images and their filenames. Dynamic Visualization: Analyze results with interactive confusion matrices. Comprehensive Reporting: Generate detailed classification reports. Test Predictions Display: View classification outcomes for test images along with predicted labels and class probabilities. For each image, detailed probability scores are provided for all possible classes. Option to generate a customizable Jupyter Notebook script based on selected inputs User-Friendly Controls: Refresh or reset workflows conveniently. This software is perfect for researchers, students, and professionals seeking an efficient, user-friendly tool for advanced image classification tasks. Note: ...